Modeling of variability-aware memristive neural networks

Document Type

Conference Proceeding

Publication Date

1-1-2023

Abstract

In recent years, memristive neuromorphic systems have gained much attention. In this work, we developed a physics-based framework to model transport in valence change memory (VCM) memristors, implemented in Verilog-A. This has enabled us to scale up and simulate the performance of these devices in a crossbar array/neural network for pattern classification, for instance. The system's performance is analyzed based on classification accuracy in different conditions. We anticipate that this will provide useful insights into the design of these systems by analyzing their performance, based on our model.

Identifier

85167873101 (Scopus)

ISBN

[9798350323108]

Publication Title

Device Research Conference Conference Digest Drc

External Full Text Location

https://doi.org/10.1109/DRC58590.2023.10187082

ISSN

15483770

Volume

2023-June

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